{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "08a727e0",
   "metadata": {},
   "source": [
    "# 自闭症（ASD）诊断分类分析项目\n",
    "\n",
    "本项目基于视线追踪和面部表情数据，使用机器学习算法对自闭症（ASD）患者和正常发育（TD）儿童进行分类诊断。\n",
    "\n",
    "## 项目目标\n",
    "- 分析ASD患者与TD儿童在视线行为和表情方面的差异\n",
    "- 构建多种分类模型进行诊断预测\n",
    "- 评估不同算法的分类性能\n",
    "\n",
    "## 数据说明\n",
    "- **ASD文件夹**: 孤独症（ASD）患者的视线及表情数据\n",
    "- **TD文件夹**: 正常发育（TD）儿童的视线及表情数据\n",
    "- **数据格式**: 每个CSV文件代表一个受试者，包含Frame（帧数）、Gaze_X（视线X坐标）、Gaze_Y（视线Y坐标）、Expression（面部表情）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "881fd135",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "字体文件路径: e:\\medical\\SimHei.ttf\n",
      "库导入完成！\n",
      "中文字体设置完成！使用字体文件：e:\\medical\\SimHei.ttf\n",
      "检查中文字体配置...\n",
      "找到的中文字体: ['SimSun', 'SimSun-ExtG', 'SimSun-ExtB', 'SimHei']\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "当前设置的字体路径: e:\\medical\\SimHei.ttf\n",
      "如果上面的中文显示正常，则字体配置成功。\n"
     ]
    }
   ],
   "source": [
    "# 导入必要的库\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import glob\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from matplotlib.font_manager import FontProperties\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# 设置中文字体\n",
    "# 使用绝对路径\n",
    "font_path = r'e:\\medical\\SimHei.ttf'  # 指定SimHei字体文件的绝对路径\n",
    "chinese_font = FontProperties(fname=font_path)\n",
    "\n",
    "# 设置matplotlib全局字体\n",
    "plt.rcParams['font.family'] = 'sans-serif'\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置matplotlib的默认中文字体\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决负号显示问题\n",
    "plt.rcParams['svg.fonttype'] = 'none'  # 确保SVG输出正确显示字体\n",
    "\n",
    "# 设置标题字体大小\n",
    "plt.rcParams['figure.titlesize'] = 18\n",
    "plt.rcParams['figure.titleweight'] = 'bold'\n",
    "\n",
    "# 注册字体到matplotlib字体管理器，确保它可以被找到\n",
    "import matplotlib.font_manager as fm\n",
    "# 验证字体是否可以被找到\n",
    "font_found = fm.fontManager.findfont(chinese_font)\n",
    "print(f\"字体文件路径: {font_found}\")\n",
    "\n",
    "print(\"库导入完成！\")\n",
    "print(f\"中文字体设置完成！使用字体文件：{font_path}\")\n",
    "\n",
    "# 检查中文字体是否正确加载并显示可用中文字体\n",
    "def check_chinese_font_availability():\n",
    "    print(\"检查中文字体配置...\")\n",
    "    # 查找SimHei字体\n",
    "    import matplotlib.font_manager as fm\n",
    "    fonts = [f.name for f in fm.fontManager.ttflist]\n",
    "    chinese_fonts = [f for f in fonts if 'SimHei' in f or '黑体' in f or 'SimSun' in f or '宋体' in f]\n",
    "    \n",
    "    if chinese_fonts:\n",
    "        print(f\"找到的中文字体: {chinese_fonts}\")\n",
    "    else:\n",
    "        print(\"未找到内置中文字体\")\n",
    "    \n",
    "    # 测试字体渲染\n",
    "    plt.figure(figsize=(8, 2))\n",
    "    plt.text(0.5, 0.5, '中文字体测试 - 自闭症诊断分类分析', \n",
    "             fontproperties=chinese_font, fontsize=16,\n",
    "             ha='center', va='center')\n",
    "    plt.gca().set_axis_off()\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    print(f\"当前设置的字体路径: {font_path}\")\n",
    "    print(\"如果上面的中文显示正常，则字体配置成功。\")\n",
    "\n",
    "# 运行检查\n",
    "check_chinese_font_availability()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "36af2c91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始加载数据...\n",
      "正在加载 ASD 数据，共找到 124 个文件...\n",
      "ASD 数据加载完成，成功加载 124 个受试者的数据\n",
      "正在加载 TD 数据，共找到 145 个文件...\n",
      "TD 数据加载完成，成功加载 145 个受试者的数据\n",
      "\n",
      "数据加载摘要：\n",
      "ASD受试者数量: 124\n",
      "TD受试者数量: 145\n",
      "总受试者数量: 269\n"
     ]
    }
   ],
   "source": [
    "# 数据加载函数\n",
    "def load_data_from_folder(folder_path, label, max_frames=2000):\n",
    "    \"\"\"\n",
    "    从指定文件夹加载CSV数据\n",
    "    \n",
    "    Parameters:\n",
    "    folder_path: 文件夹路径\n",
    "    label: 标签（ASD或TD）\n",
    "    max_frames: 最大帧数，默认2000帧\n",
    "    \n",
    "    Returns:\n",
    "    subjects_data: 包含所有受试者数据的列表\n",
    "    \"\"\"\n",
    "    subjects_data = []\n",
    "    csv_files = glob.glob(os.path.join(folder_path, \"*.csv\"))\n",
    "    \n",
    "    print(f\"正在加载 {label} 数据，共找到 {len(csv_files)} 个文件...\")\n",
    "    \n",
    "    for i, file_path in enumerate(csv_files):\n",
    "        try:\n",
    "            # 读取CSV文件\n",
    "            df = pd.read_csv(file_path)\n",
    "            \n",
    "            # 检查数据格式\n",
    "            if len(df.columns) < 4:\n",
    "                print(f\"警告：文件 {os.path.basename(file_path)} 列数不足，跳过\")\n",
    "                continue\n",
    "            \n",
    "            # 截取前max_frames帧数据\n",
    "            df_truncated = df.head(max_frames).copy()\n",
    "            \n",
    "            # 如果数据不足max_frames帧，用最后一行数据填充\n",
    "            if len(df_truncated) < max_frames:\n",
    "                last_row = df_truncated.iloc[-1:].copy()\n",
    "                padding_rows = max_frames - len(df_truncated)\n",
    "                padding_data = pd.concat([last_row] * padding_rows, ignore_index=True)\n",
    "                df_truncated = pd.concat([df_truncated, padding_data], ignore_index=True)\n",
    "            \n",
    "            # 添加标签列\n",
    "            df_truncated['Label'] = label\n",
    "            df_truncated['Subject_ID'] = f\"{label}_{i+1}\"\n",
    "            \n",
    "            subjects_data.append(df_truncated)\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f\"读取文件 {os.path.basename(file_path)} 时出错: {str(e)}\")\n",
    "            continue\n",
    "    \n",
    "    print(f\"{label} 数据加载完成，成功加载 {len(subjects_data)} 个受试者的数据\")\n",
    "    return subjects_data\n",
    "\n",
    "# 设置数据路径\n",
    "asd_folder = \"ASD\"\n",
    "td_folder = \"TD\"\n",
    "\n",
    "# 加载ASD和TD数据\n",
    "print(\"开始加载数据...\")\n",
    "asd_data = load_data_from_folder(asd_folder, \"ASD\", max_frames=2000)\n",
    "td_data = load_data_from_folder(td_folder, \"TD\", max_frames=2000)\n",
    "\n",
    "print(f\"\\n数据加载摘要：\")\n",
    "print(f\"ASD受试者数量: {len(asd_data)}\")\n",
    "print(f\"TD受试者数量: {len(td_data)}\")\n",
    "print(f\"总受试者数量: {len(asd_data) + len(td_data)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6efc032d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始特征提取...\n",
      "特征提取完成！\n",
      "特征维度: (269, 17)\n",
      "样本数量: 269\n",
      "ASD样本数: 124\n",
      "TD样本数: 145\n",
      "\n",
      "提取的特征前5行：\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gaze_x_mean</th>\n",
       "      <th>gaze_x_std</th>\n",
       "      <th>gaze_x_min</th>\n",
       "      <th>gaze_x_max</th>\n",
       "      <th>gaze_x_median</th>\n",
       "      <th>gaze_y_mean</th>\n",
       "      <th>gaze_y_std</th>\n",
       "      <th>gaze_y_min</th>\n",
       "      <th>gaze_y_max</th>\n",
       "      <th>gaze_y_median</th>\n",
       "      <th>gaze_distance_mean</th>\n",
       "      <th>gaze_distance_std</th>\n",
       "      <th>gaze_distance_max</th>\n",
       "      <th>expression_mean</th>\n",
       "      <th>expression_std</th>\n",
       "      <th>expression_mode</th>\n",
       "      <th>expression_change_frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.237387</td>\n",
       "      <td>0.083291</td>\n",
       "      <td>-0.425527</td>\n",
       "      <td>-0.020738</td>\n",
       "      <td>-0.208652</td>\n",
       "      <td>0.109408</td>\n",
       "      <td>0.069778</td>\n",
       "      <td>-0.161018</td>\n",
       "      <td>0.337163</td>\n",
       "      <td>0.112627</td>\n",
       "      <td>0.050505</td>\n",
       "      <td>0.062021</td>\n",
       "      <td>0.439337</td>\n",
       "      <td>4.1575</td>\n",
       "      <td>1.856992</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.313247</td>\n",
       "      <td>0.045940</td>\n",
       "      <td>-0.455957</td>\n",
       "      <td>-0.091011</td>\n",
       "      <td>-0.314971</td>\n",
       "      <td>0.075540</td>\n",
       "      <td>0.083699</td>\n",
       "      <td>-0.291304</td>\n",
       "      <td>0.339329</td>\n",
       "      <td>0.077781</td>\n",
       "      <td>0.060210</td>\n",
       "      <td>0.041841</td>\n",
       "      <td>0.319906</td>\n",
       "      <td>1.8080</td>\n",
       "      <td>2.561722</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.366016</td>\n",
       "      <td>0.043802</td>\n",
       "      <td>-0.487417</td>\n",
       "      <td>-0.065308</td>\n",
       "      <td>-0.367895</td>\n",
       "      <td>0.116133</td>\n",
       "      <td>0.085747</td>\n",
       "      <td>-0.214857</td>\n",
       "      <td>0.359475</td>\n",
       "      <td>0.127611</td>\n",
       "      <td>0.063990</td>\n",
       "      <td>0.043840</td>\n",
       "      <td>0.409371</td>\n",
       "      <td>4.6205</td>\n",
       "      <td>0.982834</td>\n",
       "      <td>4</td>\n",
       "      <td>0.2935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.359024</td>\n",
       "      <td>0.056500</td>\n",
       "      <td>-0.498556</td>\n",
       "      <td>-0.093249</td>\n",
       "      <td>-0.365273</td>\n",
       "      <td>0.087768</td>\n",
       "      <td>0.099609</td>\n",
       "      <td>-0.243339</td>\n",
       "      <td>0.330026</td>\n",
       "      <td>0.105940</td>\n",
       "      <td>0.072063</td>\n",
       "      <td>0.056377</td>\n",
       "      <td>0.401837</td>\n",
       "      <td>4.1465</td>\n",
       "      <td>2.042578</td>\n",
       "      <td>6</td>\n",
       "      <td>0.3790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.353257</td>\n",
       "      <td>0.047172</td>\n",
       "      <td>-0.483185</td>\n",
       "      <td>-0.109099</td>\n",
       "      <td>-0.356342</td>\n",
       "      <td>0.077231</td>\n",
       "      <td>0.105544</td>\n",
       "      <td>-0.314011</td>\n",
       "      <td>0.439062</td>\n",
       "      <td>0.086231</td>\n",
       "      <td>0.067480</td>\n",
       "      <td>0.054189</td>\n",
       "      <td>0.430959</td>\n",
       "      <td>5.0590</td>\n",
       "      <td>1.205506</td>\n",
       "      <td>6</td>\n",
       "      <td>0.2185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gaze_x_mean  gaze_x_std  gaze_x_min  gaze_x_max  gaze_x_median  \\\n",
       "0    -0.237387    0.083291   -0.425527   -0.020738      -0.208652   \n",
       "1    -0.313247    0.045940   -0.455957   -0.091011      -0.314971   \n",
       "2    -0.366016    0.043802   -0.487417   -0.065308      -0.367895   \n",
       "3    -0.359024    0.056500   -0.498556   -0.093249      -0.365273   \n",
       "4    -0.353257    0.047172   -0.483185   -0.109099      -0.356342   \n",
       "\n",
       "   gaze_y_mean  gaze_y_std  gaze_y_min  gaze_y_max  gaze_y_median  \\\n",
       "0     0.109408    0.069778   -0.161018    0.337163       0.112627   \n",
       "1     0.075540    0.083699   -0.291304    0.339329       0.077781   \n",
       "2     0.116133    0.085747   -0.214857    0.359475       0.127611   \n",
       "3     0.087768    0.099609   -0.243339    0.330026       0.105940   \n",
       "4     0.077231    0.105544   -0.314011    0.439062       0.086231   \n",
       "\n",
       "   gaze_distance_mean  gaze_distance_std  gaze_distance_max  expression_mean  \\\n",
       "0            0.050505           0.062021           0.439337           4.1575   \n",
       "1            0.060210           0.041841           0.319906           1.8080   \n",
       "2            0.063990           0.043840           0.409371           4.6205   \n",
       "3            0.072063           0.056377           0.401837           4.1465   \n",
       "4            0.067480           0.054189           0.430959           5.0590   \n",
       "\n",
       "   expression_std  expression_mode  expression_change_frequency  \n",
       "0        1.856992                6                       0.1265  \n",
       "1        2.561722                0                       0.2180  \n",
       "2        0.982834                4                       0.2935  \n",
       "3        2.042578                6                       0.3790  \n",
       "4        1.205506                6                       0.2185  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 特征工程：提取统计特征\n",
    "def extract_features(subject_data):\n",
    "    \"\"\"\n",
    "    从每个受试者的时序数据中提取统计特征\n",
    "    \n",
    "    Parameters:\n",
    "    subject_data: 单个受试者的DataFrame\n",
    "    \n",
    "    Returns:\n",
    "    features: 提取的特征向量\n",
    "    \"\"\"\n",
    "    features = {}\n",
    "    \n",
    "    # 视线特征（Gaze_X, Gaze_Y）\n",
    "    gaze_x = subject_data['Gaze_X'].dropna()\n",
    "    gaze_y = subject_data['Gaze_Y'].dropna()\n",
    "    \n",
    "    # 基本统计特征\n",
    "    features['gaze_x_mean'] = gaze_x.mean()\n",
    "    features['gaze_x_std'] = gaze_x.std()\n",
    "    features['gaze_x_min'] = gaze_x.min()\n",
    "    features['gaze_x_max'] = gaze_x.max()\n",
    "    features['gaze_x_median'] = gaze_x.median()\n",
    "    \n",
    "    features['gaze_y_mean'] = gaze_y.mean()\n",
    "    features['gaze_y_std'] = gaze_y.std()\n",
    "    features['gaze_y_min'] = gaze_y.min()\n",
    "    features['gaze_y_max'] = gaze_y.max()\n",
    "    features['gaze_y_median'] = gaze_y.median()\n",
    "    \n",
    "    # 视线移动距离和速度特征\n",
    "    gaze_diff_x = np.diff(gaze_x)\n",
    "    gaze_diff_y = np.diff(gaze_y)\n",
    "    gaze_distance = np.sqrt(gaze_diff_x**2 + gaze_diff_y**2)\n",
    "    \n",
    "    features['gaze_distance_mean'] = gaze_distance.mean()\n",
    "    features['gaze_distance_std'] = gaze_distance.std()\n",
    "    features['gaze_distance_max'] = gaze_distance.max()\n",
    "    \n",
    "    # 表情特征\n",
    "    expression = subject_data['Expression'].dropna()\n",
    "    features['expression_mean'] = expression.mean()\n",
    "    features['expression_std'] = expression.std()\n",
    "    features['expression_mode'] = expression.mode().iloc[0] if not expression.mode().empty else 0\n",
    "    \n",
    "    # 表情变化频率\n",
    "    expression_changes = np.sum(np.diff(expression) != 0)\n",
    "    features['expression_change_frequency'] = expression_changes / len(expression) if len(expression) > 1 else 0\n",
    "    \n",
    "    return features\n",
    "\n",
    "print(\"开始特征提取...\")\n",
    "\n",
    "# 为所有受试者提取特征\n",
    "all_features = []\n",
    "all_labels = []\n",
    "\n",
    "# 处理ASD数据\n",
    "for subject_data in asd_data:\n",
    "    features = extract_features(subject_data)\n",
    "    all_features.append(features)\n",
    "    all_labels.append(0)  # ASD标记为0\n",
    "\n",
    "# 处理TD数据\n",
    "for subject_data in td_data:\n",
    "    features = extract_features(subject_data)\n",
    "    all_features.append(features)\n",
    "    all_labels.append(1)  # TD标记为1\n",
    "\n",
    "# 转换为DataFrame\n",
    "features_df = pd.DataFrame(all_features)\n",
    "labels = np.array(all_labels)\n",
    "\n",
    "print(f\"特征提取完成！\")\n",
    "print(f\"特征维度: {features_df.shape}\")\n",
    "print(f\"样本数量: {len(labels)}\")\n",
    "print(f\"ASD样本数: {np.sum(labels == 0)}\")\n",
    "print(f\"TD样本数: {np.sum(labels == 1)}\")\n",
    "\n",
    "# 显示特征的前几行\n",
    "print(f\"\\n提取的特征前5行：\")\n",
    "features_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a95c33d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始数据集划分...\n",
      "训练集大小: 215\n",
      "测试集大小: 54\n",
      "训练集中ASD样本数: 99\n",
      "训练集中TD样本数: 116\n",
      "测试集中ASD样本数: 25\n",
      "测试集中TD样本数: 29\n",
      "\n",
      "数据标准化完成！\n",
      "训练集特征范围: [-4.313, 4.704]\n",
      "测试集特征范围: [-4.870, 3.485]\n"
     ]
    }
   ],
   "source": [
    "# 数据集划分与标准化\n",
    "print(\"开始数据集划分...\")\n",
    "\n",
    "# 处理缺失值\n",
    "features_df = features_df.fillna(features_df.mean())\n",
    "\n",
    "# 划分训练集和测试集（保证类别均衡）\n",
    "X = features_df.values\n",
    "y = labels\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, \n",
    "    test_size=0.2, \n",
    "    random_state=42, \n",
    "    stratify=y  # 保证训练集和测试集中ASD和TD样本比例一致\n",
    ")\n",
    "\n",
    "print(f\"训练集大小: {X_train.shape[0]}\")\n",
    "print(f\"测试集大小: {X_test.shape[0]}\")\n",
    "print(f\"训练集中ASD样本数: {np.sum(y_train == 0)}\")\n",
    "print(f\"训练集中TD样本数: {np.sum(y_train == 1)}\")\n",
    "print(f\"测试集中ASD样本数: {np.sum(y_test == 0)}\")\n",
    "print(f\"测试集中TD样本数: {np.sum(y_test == 1)}\")\n",
    "\n",
    "# 特征标准化\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n",
    "\n",
    "print(f\"\\n数据标准化完成！\")\n",
    "print(f\"训练集特征范围: [{X_train_scaled.min():.3f}, {X_train_scaled.max():.3f}]\")\n",
    "print(f\"测试集特征范围: [{X_test_scaled.min():.3f}, {X_test_scaled.max():.3f}]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a89eae6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始训练多种分类模型...\n",
      "\n",
      "正在训练 逻辑回归...\n",
      "逻辑回归 在测试集上的准确率: 0.7407\n",
      "逻辑回归 5折交叉验证平均准确率: 0.7674 (±0.0294)\n",
      "\n",
      "正在训练 支持向量机...\n",
      "支持向量机 在测试集上的准确率: 0.7407\n",
      "支持向量机 5折交叉验证平均准确率: 0.7488 (±0.0902)\n",
      "\n",
      "正在训练 随机森林...\n",
      "随机森林 在测试集上的准确率: 0.7407\n",
      "随机森林 5折交叉验证平均准确率: 0.7349 (±0.1367)\n",
      "\n",
      "所有模型训练完成！\n",
      "\n",
      "模型性能排序：\n",
      "1. 逻辑回归: 0.7407\n",
      "2. 支持向量机: 0.7407\n",
      "3. 随机森林: 0.7407\n"
     ]
    }
   ],
   "source": [
    "# 多种分类算法建模与训练\n",
    "print(\"开始训练多种分类模型...\")\n",
    "\n",
    "# 定义分类器\n",
    "classifiers = {\n",
    "    '逻辑回归': LogisticRegression(random_state=42, max_iter=1000),\n",
    "    '支持向量机': SVC(random_state=42, probability=True),\n",
    "    '随机森林': RandomForestClassifier(n_estimators=100, random_state=42)\n",
    "}\n",
    "\n",
    "# 存储模型和结果\n",
    "trained_models = {}\n",
    "model_scores = {}\n",
    "\n",
    "# 训练每个模型\n",
    "for name, classifier in classifiers.items():\n",
    "    print(f\"\\n正在训练 {name}...\")\n",
    "    \n",
    "    # 训练模型\n",
    "    classifier.fit(X_train_scaled, y_train)\n",
    "    trained_models[name] = classifier\n",
    "    \n",
    "    # 预测\n",
    "    y_pred = classifier.predict(X_test_scaled)\n",
    "    \n",
    "    # 计算准确率\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    model_scores[name] = accuracy\n",
    "    \n",
    "    print(f\"{name} 在测试集上的准确率: {accuracy:.4f}\")\n",
    "    \n",
    "    # 交叉验证\n",
    "    cv_scores = cross_val_score(classifier, X_train_scaled, y_train, cv=5)\n",
    "    print(f\"{name} 5折交叉验证平均准确率: {cv_scores.mean():.4f} (±{cv_scores.std()*2:.4f})\")\n",
    "\n",
    "print(f\"\\n所有模型训练完成！\")\n",
    "print(f\"\\n模型性能排序：\")\n",
    "sorted_models = sorted(model_scores.items(), key=lambda x: x[1], reverse=True)\n",
    "for i, (name, score) in enumerate(sorted_models, 1):\n",
    "    print(f\"{i}. {name}: {score:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f37e18c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始详细模型评估...\n",
      "\n",
      "==================================================\n",
      "逻辑回归 详细评估报告\n",
      "==================================================\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ASD       0.87      0.52      0.65        25\n",
      "          TD       0.69      0.93      0.79        29\n",
      "\n",
      "    accuracy                           0.74        54\n",
      "   macro avg       0.78      0.73      0.72        54\n",
      "weighted avg       0.77      0.74      0.73        54\n",
      "\n",
      "\n",
      "混淆矩阵:\n",
      "预测\\实际    ASD    TD\n",
      "ASD         13    12\n",
      "TD           2    27\n",
      "\n",
      "==================================================\n",
      "支持向量机 详细评估报告\n",
      "==================================================\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ASD       0.79      0.60      0.68        25\n",
      "          TD       0.71      0.86      0.78        29\n",
      "\n",
      "    accuracy                           0.74        54\n",
      "   macro avg       0.75      0.73      0.73        54\n",
      "weighted avg       0.75      0.74      0.74        54\n",
      "\n",
      "\n",
      "混淆矩阵:\n",
      "预测\\实际    ASD    TD\n",
      "ASD         15    10\n",
      "TD           4    25\n",
      "\n",
      "==================================================\n",
      "随机森林 详细评估报告\n",
      "==================================================\n",
      "分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ASD       0.74      0.68      0.71        25\n",
      "          TD       0.74      0.79      0.77        29\n",
      "\n",
      "    accuracy                           0.74        54\n",
      "   macro avg       0.74      0.74      0.74        54\n",
      "weighted avg       0.74      0.74      0.74        54\n",
      "\n",
      "\n",
      "混淆矩阵:\n",
      "预测\\实际    ASD    TD\n",
      "ASD         17     8\n",
      "TD           6    23\n",
      "\n",
      "详细评估完成！\n"
     ]
    }
   ],
   "source": [
    "# 详细模型评估\n",
    "print(\"开始详细模型评估...\")\n",
    "\n",
    "# 为每个模型生成详细评估报告\n",
    "evaluation_results = {}\n",
    "\n",
    "for name, model in trained_models.items():\n",
    "    print(f\"\\n{'='*50}\")\n",
    "    print(f\"{name} 详细评估报告\")\n",
    "    print(f\"{'='*50}\")\n",
    "    \n",
    "    # 预测\n",
    "    y_pred = model.predict(X_test_scaled)\n",
    "    \n",
    "    # 分类报告\n",
    "    report = classification_report(y_test, y_pred, \n",
    "                                 target_names=['ASD', 'TD'], \n",
    "                                 output_dict=True)\n",
    "    \n",
    "    print(\"分类报告:\")\n",
    "    print(classification_report(y_test, y_pred, target_names=['ASD', 'TD']))\n",
    "    \n",
    "    # 混淆矩阵\n",
    "    cm = confusion_matrix(y_test, y_pred)\n",
    "    print(f\"\\n混淆矩阵:\")\n",
    "    print(f\"预测\\\\实际    ASD    TD\")\n",
    "    print(f\"ASD        {cm[0,0]:3d}   {cm[0,1]:3d}\")\n",
    "    print(f\"TD         {cm[1,0]:3d}   {cm[1,1]:3d}\")\n",
    "    \n",
    "    # 存储评估结果\n",
    "    evaluation_results[name] = {\n",
    "        'predictions': y_pred,\n",
    "        'classification_report': report,\n",
    "        'confusion_matrix': cm,\n",
    "        'accuracy': report['accuracy']\n",
    "    }\n",
    "\n",
    "print(f\"\\n详细评估完成！\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8c047905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 结果可视化 - 模型性能对比\n",
    "plt.style.use('default')\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "fig.suptitle('自闭症诊断分类模型性能分析', fontsize=16, fontproperties=chinese_font)\n",
    "\n",
    "# 1. 模型准确率对比柱状图\n",
    "ax1 = axes[0, 0]\n",
    "models = list(model_scores.keys())\n",
    "scores = list(model_scores.values())\n",
    "bars = ax1.bar(models, scores, color=['#1f77b4', '#ff7f0e', '#2ca02c'])\n",
    "ax1.set_title('各模型测试集准确率对比', fontproperties=chinese_font)\n",
    "ax1.set_ylabel('准确率', fontproperties=chinese_font)\n",
    "ax1.set_ylim(0, 1)\n",
    "\n",
    "# 在柱状图上添加数值\n",
    "for bar, score in zip(bars, scores):\n",
    "    height = bar.get_height()\n",
    "    ax1.text(bar.get_x() + bar.get_width()/2., height + 0.01,\n",
    "             f'{score:.3f}', ha='center', va='bottom')\n",
    "\n",
    "# 设置x轴标签使用中文字体\n",
    "for label in ax1.get_xticklabels():\n",
    "    label.set_fontproperties(chinese_font)\n",
    "\n",
    "# 2. 各模型性能指标雷达图数据准备\n",
    "metrics_data = {}\n",
    "for name, results in evaluation_results.items():\n",
    "    report = results['classification_report']\n",
    "    metrics_data[name] = {\n",
    "        'ASD精确率': report['ASD']['precision'],\n",
    "        'ASD召回率': report['ASD']['recall'],\n",
    "        'ASD F1分数': report['ASD']['f1-score'],\n",
    "        'TD精确率': report['TD']['precision'],\n",
    "        'TD召回率': report['TD']['recall'],\n",
    "        'TD F1分数': report['TD']['f1-score']\n",
    "    }\n",
    "\n",
    "# 3. 精确率对比\n",
    "ax2 = axes[0, 1]\n",
    "asd_precision = [metrics_data[name]['ASD精确率'] for name in models]\n",
    "td_precision = [metrics_data[name]['TD精确率'] for name in models]\n",
    "\n",
    "x = np.arange(len(models))\n",
    "width = 0.35\n",
    "\n",
    "ax2.bar(x - width/2, asd_precision, width, label='ASD精确率', color='#ff7f0e', alpha=0.8)\n",
    "ax2.bar(x + width/2, td_precision, width, label='TD精确率', color='#2ca02c', alpha=0.8)\n",
    "\n",
    "ax2.set_title('各模型精确率对比', fontproperties=chinese_font)\n",
    "ax2.set_ylabel('精确率', fontproperties=chinese_font)\n",
    "ax2.set_xlabel('模型', fontproperties=chinese_font)\n",
    "ax2.set_xticks(x)\n",
    "ax2.set_xticklabels(models, fontproperties=chinese_font)\n",
    "ax2.legend(prop=chinese_font)\n",
    "ax2.set_ylim(0, 1.1)\n",
    "\n",
    "# 4. 召回率对比\n",
    "ax3 = axes[1, 0]\n",
    "asd_recall = [metrics_data[name]['ASD召回率'] for name in models]\n",
    "td_recall = [metrics_data[name]['TD召回率'] for name in models]\n",
    "\n",
    "ax3.bar(x - width/2, asd_recall, width, label='ASD召回率', color='#d62728', alpha=0.8)\n",
    "ax3.bar(x + width/2, td_recall, width, label='TD召回率', color='#9467bd', alpha=0.8)\n",
    "\n",
    "ax3.set_title('各模型召回率对比', fontproperties=chinese_font)\n",
    "ax3.set_ylabel('召回率', fontproperties=chinese_font)\n",
    "ax3.set_xlabel('模型', fontproperties=chinese_font)\n",
    "ax3.set_xticks(x)\n",
    "ax3.set_xticklabels(models, fontproperties=chinese_font)\n",
    "ax3.legend(prop=chinese_font)\n",
    "ax3.set_ylim(0, 1.1)\n",
    "\n",
    "# 5. F1分数对比\n",
    "ax4 = axes[1, 1]\n",
    "asd_f1 = [metrics_data[name]['ASD F1分数'] for name in models]\n",
    "td_f1 = [metrics_data[name]['TD F1分数'] for name in models]\n",
    "\n",
    "ax4.bar(x - width/2, asd_f1, width, label='ASD F1分数', color='#8c564b', alpha=0.8)\n",
    "ax4.bar(x + width/2, td_f1, width, label='TD F1分数', color='#e377c2', alpha=0.8)\n",
    "\n",
    "ax4.set_title('各模型F1分数对比', fontproperties=chinese_font)\n",
    "ax4.set_ylabel('F1分数', fontproperties=chinese_font)\n",
    "ax4.set_xlabel('模型', fontproperties=chinese_font)\n",
    "ax4.set_xticks(x)\n",
    "ax4.set_xticklabels(models, fontproperties=chinese_font)\n",
    "ax4.legend(prop=chinese_font)\n",
    "ax4.set_ylim(0, 1.1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb79a753",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "模型敏感性和特异性分析：\n",
      "============================================================\n",
      "模型名称         敏感性(ASD)     特异性(TD)      准确率     \n",
      "------------------------------------------------------------\n",
      "逻辑回归         0.867        0.692        0.741   \n",
      "支持向量机        0.789        0.714        0.741   \n",
      "随机森林         0.739        0.742        0.741   \n",
      "------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# 混淆矩阵可视化\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "fig.suptitle('各模型混淆矩阵', fontsize=16, fontproperties=chinese_font)\n",
    "\n",
    "for i, (name, results) in enumerate(evaluation_results.items()):\n",
    "    cm = results['confusion_matrix']\n",
    "    \n",
    "    # 创建热力图\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', \n",
    "                xticklabels=['ASD', 'TD'], \n",
    "                yticklabels=['ASD', 'TD'],\n",
    "                ax=axes[i])\n",
    "    \n",
    "    axes[i].set_title(f'{name}\\n准确率: {results[\"accuracy\"]:.3f}', fontproperties=chinese_font)\n",
    "    axes[i].set_xlabel('预测标签', fontproperties=chinese_font)\n",
    "    axes[i].set_ylabel('真实标签', fontproperties=chinese_font)\n",
    "    \n",
    "    # 确保刻度标签也使用中文字体\n",
    "    for label in axes[i].get_xticklabels() + axes[i].get_yticklabels():\n",
    "        label.set_fontproperties(chinese_font)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=0.85)  # 为主标题留出空间\n",
    "plt.show()\n",
    "\n",
    "# 计算每个模型的敏感性和特异性\n",
    "print(\"模型敏感性和特异性分析：\")\n",
    "print(\"=\"*60)\n",
    "print(f\"{'模型名称':<12} {'敏感性(ASD)':<12} {'特异性(TD)':<12} {'准确率':<8}\")\n",
    "print(\"-\"*60)\n",
    "\n",
    "for name, results in evaluation_results.items():\n",
    "    cm = results['confusion_matrix']\n",
    "    \n",
    "    # 敏感性 = TP/(TP+FN) - 正确识别ASD的比例\n",
    "    sensitivity = cm[0,0] / (cm[0,0] + cm[1,0]) if (cm[0,0] + cm[1,0]) > 0 else 0\n",
    "    \n",
    "    # 特异性 = TN/(TN+FP) - 正确识别TD的比例  \n",
    "    specificity = cm[1,1] / (cm[1,1] + cm[0,1]) if (cm[1,1] + cm[0,1]) > 0 else 0\n",
    "    \n",
    "    accuracy = results['accuracy']\n",
    "    \n",
    "    print(f\"{name:<12} {sensitivity:<12.3f} {specificity:<12.3f} {accuracy:<8.3f}\")\n",
    "\n",
    "print(\"-\"*60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "31b5ef8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征重要性分析...\n",
      "前10个最重要的特征：\n",
      "                  特征名称       重要性\n",
      "11   gaze_distance_std  0.138770\n",
      "6           gaze_y_std  0.130681\n",
      "12   gaze_distance_max  0.119149\n",
      "10  gaze_distance_mean  0.062929\n",
      "3           gaze_x_max  0.057166\n",
      "1           gaze_x_std  0.056718\n",
      "7           gaze_y_min  0.051923\n",
      "9        gaze_y_median  0.050930\n",
      "8           gaze_y_max  0.047352\n",
      "0          gaze_x_mean  0.046016\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "ASD和TD在重要特征上的统计差异：\n",
      "======================================================================\n",
      "特征名称                      ASD均值      TD均值       差异        \n",
      "----------------------------------------------------------------------\n",
      "gaze_distance_std         0.0523     0.0406     0.0117    \n",
      "gaze_y_std                0.1013     0.0771     0.0242    \n",
      "gaze_distance_max         0.4004     0.3197     0.0807    \n",
      "gaze_distance_mean        0.0698     0.0609     0.0089    \n",
      "gaze_x_max                -0.0772    -0.1345    0.0573    \n",
      "----------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 特征重要性分析\n",
    "print(\"特征重要性分析...\")\n",
    "\n",
    "# 使用随机森林的特征重要性\n",
    "rf_model = trained_models['随机森林']\n",
    "feature_importance = rf_model.feature_importances_\n",
    "feature_names = features_df.columns\n",
    "\n",
    "# 创建特征重要性DataFrame\n",
    "importance_df = pd.DataFrame({\n",
    "    '特征名称': feature_names,\n",
    "    '重要性': feature_importance\n",
    "}).sort_values('重要性', ascending=False)\n",
    "\n",
    "print(\"前10个最重要的特征：\")\n",
    "print(importance_df.head(10))\n",
    "\n",
    "# 特征重要性可视化\n",
    "plt.figure(figsize=(12, 8))\n",
    "top_features = importance_df.head(10)\n",
    "\n",
    "# 先设置全局字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "bars = plt.barh(range(len(top_features)), top_features['重要性'])\n",
    "plt.yticks(range(len(top_features)), top_features['特征名称'], fontproperties=chinese_font)\n",
    "plt.xlabel('特征重要性', fontproperties=chinese_font, fontsize=12)\n",
    "plt.title('前10个最重要特征（基于随机森林）', fontproperties=chinese_font, fontsize=14)\n",
    "plt.gca().invert_yaxis()\n",
    "\n",
    "# 添加数值标签\n",
    "for i, bar in enumerate(bars):\n",
    "    width = bar.get_width()\n",
    "    plt.text(width + 0.001, bar.get_y() + bar.get_height()/2, \n",
    "             f'{width:.3f}', ha='left', va='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 分析ASD和TD在重要特征上的差异\n",
    "print(\"\\nASD和TD在重要特征上的统计差异：\")\n",
    "print(\"=\"*70)\n",
    "\n",
    "# 重新构建原始特征数据以便分析\n",
    "asd_features = []\n",
    "td_features = []\n",
    "\n",
    "for i, label in enumerate(labels):\n",
    "    if label == 0:  # ASD\n",
    "        asd_features.append(features_df.iloc[i])\n",
    "    else:  # TD\n",
    "        td_features.append(features_df.iloc[i])\n",
    "\n",
    "asd_df = pd.DataFrame(asd_features)\n",
    "td_df = pd.DataFrame(td_features)\n",
    "\n",
    "print(f\"{'特征名称':<25} {'ASD均值':<10} {'TD均值':<10} {'差异':<10}\")\n",
    "print(\"-\"*70)\n",
    "\n",
    "for feature in top_features['特征名称'].head(5):\n",
    "    asd_mean = asd_df[feature].mean()\n",
    "    td_mean = td_df[feature].mean()\n",
    "    difference = abs(asd_mean - td_mean)\n",
    "    \n",
    "    print(f\"{feature:<25} {asd_mean:<10.4f} {td_mean:<10.4f} {difference:<10.4f}\")\n",
    "\n",
    "print(\"-\"*70)\n",
    "\n",
    "# 可视化前5个重要特征的ASD和TD差异\n",
    "plt.figure(figsize=(14, 8))\n",
    "top_5_features = top_features['特征名称'][:5]\n",
    "\n",
    "x = np.arange(len(top_5_features))\n",
    "width = 0.35\n",
    "\n",
    "# 重新设置字体以确保一致性\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 计算ASD和TD在这些特征上的均值\n",
    "asd_means = [asd_df[feature].mean() for feature in top_5_features]\n",
    "td_means = [td_df[feature].mean() for feature in top_5_features]\n",
    "\n",
    "# 绘制柱状图\n",
    "plt.bar(x - width/2, asd_means, width, label='ASD', color='#ff7f0e', alpha=0.8)\n",
    "plt.bar(x + width/2, td_means, width, label='TD', color='#2ca02c', alpha=0.8)\n",
    "\n",
    "plt.xlabel('特征', fontproperties=chinese_font, fontsize=12)\n",
    "plt.ylabel('均值', fontproperties=chinese_font, fontsize=12)\n",
    "plt.title('ASD和TD在前5个重要特征上的差异', fontproperties=chinese_font, fontsize=14)\n",
    "\n",
    "# 设置x轴刻度标签的字体\n",
    "plt.xticks(x, top_5_features, fontproperties=chinese_font, rotation=30)\n",
    "plt.legend(prop=chinese_font)\n",
    "\n",
    "# 确保所有标签使用中文字体\n",
    "for label in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    label.set_fontproperties(chinese_font)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a446f570",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "自闭症（ASD）诊断分类项目总结\n",
      "================================================================================\n",
      "\n",
      "🏆 最佳模型: 逻辑回归\n",
      "📊 最高准确率: 0.7407\n",
      "\n",
      "📈 模型性能总结:\n",
      "   1. 逻辑回归: 0.7407\n",
      "   2. 支持向量机: 0.7407\n",
      "   3. 随机森林: 0.7407\n",
      "\n",
      "🔍 关键发现:\n",
      "   • 数据集包含 ASD 样本 124 个，TD 样本 145 个\n",
      "   • 提取了 17 个特征，包括视线追踪和表情相关特征\n",
      "   • 采用 8:2 比例划分训练集和测试集，保证类别均衡\n",
      "   • 最重要的5个特征: gaze_distance_std, gaze_y_std, gaze_distance_max, gaze_distance_mean, gaze_x_max\n",
      "\n",
      "💡 临床意义:\n",
      "   • 视线追踪数据在ASD诊断中具有重要价值\n",
      "   • 面部表情变化模式可以区分ASD患者和正常发育儿童\n",
      "   • 机器学习方法可以辅助ASD的早期诊断和筛查\n",
      "\n",
      "🚀 后续改进建议:\n",
      "   • 增加样本数量以提高模型泛化能力\n",
      "   • 尝试深度学习方法处理原始时序数据\n",
      "   • 结合其他生物标记物进行多模态融合\n",
      "   • 开发实时诊断系统的原型\n",
      "\n",
      "================================================================================\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 项目分析完成！结果已保存。\n"
     ]
    }
   ],
   "source": [
    "# 项目总结与结论\n",
    "print(\"=\"*80)\n",
    "print(\"自闭症（ASD）诊断分类项目总结\")\n",
    "print(\"=\"*80)\n",
    "\n",
    "# 找出最佳模型\n",
    "best_model_name = max(model_scores.items(), key=lambda x: x[1])[0]\n",
    "best_accuracy = model_scores[best_model_name]\n",
    "\n",
    "print(f\"\\n🏆 最佳模型: {best_model_name}\")\n",
    "print(f\"📊 最高准确率: {best_accuracy:.4f}\")\n",
    "\n",
    "print(f\"\\n📈 模型性能总结:\")\n",
    "for i, (name, score) in enumerate(sorted(model_scores.items(), key=lambda x: x[1], reverse=True), 1):\n",
    "    print(f\"   {i}. {name}: {score:.4f}\")\n",
    "\n",
    "print(f\"\\n🔍 关键发现:\")\n",
    "print(f\"   • 数据集包含 ASD 样本 {np.sum(labels == 0)} 个，TD 样本 {np.sum(labels == 1)} 个\")\n",
    "print(f\"   • 提取了 {features_df.shape[1]} 个特征，包括视线追踪和表情相关特征\")\n",
    "print(f\"   • 采用 8:2 比例划分训练集和测试集，保证类别均衡\")\n",
    "\n",
    "# 最重要的5个特征\n",
    "top_5_features = importance_df.head(5)['特征名称'].tolist()\n",
    "print(f\"   • 最重要的5个特征: {', '.join(top_5_features)}\")\n",
    "\n",
    "print(f\"\\n💡 临床意义:\")\n",
    "print(f\"   • 视线追踪数据在ASD诊断中具有重要价值\")\n",
    "print(f\"   • 面部表情变化模式可以区分ASD患者和正常发育儿童\")\n",
    "print(f\"   • 机器学习方法可以辅助ASD的早期诊断和筛查\")\n",
    "\n",
    "print(f\"\\n🚀 后续改进建议:\")\n",
    "print(f\"   • 增加样本数量以提高模型泛化能力\")\n",
    "print(f\"   • 尝试深度学习方法处理原始时序数据\")\n",
    "print(f\"   • 结合其他生物标记物进行多模态融合\")\n",
    "print(f\"   • 开发实时诊断系统的原型\")\n",
    "\n",
    "print(f\"\\n\" + \"=\"*80)\n",
    "\n",
    "# 创建结果可视化 - 使用更强大的中文字体处理\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# 显式设置全局字体\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei'] \n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 获取模型名称和性能分数\n",
    "model_names = list(model_scores.keys())\n",
    "scores = list(model_scores.values())\n",
    "\n",
    "# 创建水平条形图而非垂直条形图\n",
    "bars = plt.barh(range(len(model_names)), scores, color=['#1f77b4', '#ff7f0e', '#2ca02c'])\n",
    "plt.yticks(range(len(model_names)), model_names, fontproperties=chinese_font, fontsize=12)\n",
    "plt.xlim(0, 1)\n",
    "\n",
    "# 添加标题和标签\n",
    "plt.title('自闭症诊断模型性能总结', fontproperties=chinese_font, fontsize=16)\n",
    "plt.xlabel('准确率', fontproperties=chinese_font, fontsize=14)\n",
    "plt.ylabel('模型', fontproperties=chinese_font, fontsize=14)\n",
    "\n",
    "# 确保所有轴标签使用正确的字体\n",
    "for label in plt.gca().get_xticklabels() + plt.gca().get_yticklabels():\n",
    "    label.set_fontproperties(chinese_font)\n",
    "\n",
    "# 在条形上添加数值标签\n",
    "for i, bar in enumerate(bars):\n",
    "    width = bar.get_width()\n",
    "    plt.text(width + 0.01, bar.get_y() + bar.get_height()/2, \n",
    "             f'{width:.3f}', ha='left', va='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 保存模型性能结果\n",
    "results_summary = {\n",
    "    'model_scores': model_scores,\n",
    "    'best_model': best_model_name,\n",
    "    'best_accuracy': best_accuracy,\n",
    "    'feature_importance': importance_df.to_dict(),\n",
    "    'dataset_info': {\n",
    "        'total_samples': len(labels),\n",
    "        'asd_samples': int(np.sum(labels == 0)),\n",
    "        'td_samples': int(np.sum(labels == 1)),\n",
    "        'features_count': features_df.shape[1]\n",
    "    }\n",
    "}\n",
    "\n",
    "print(f\"✅ 项目分析完成！结果已保存。\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "machine",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
